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Int. J. Financial Stud. 2018, 6(1), 19; https://doi.org/10.3390/ijfs6010019

Noise Reduction in a Reputation Index

1
Santander UK, 2 Triton Square, Regent’s Place, London NW1 3AN, UK
2
Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
3
Laboratoire d’Excellence sur la Régulation Financière (LabEx ReFi), 75272 Paris, France
Received: 1 January 2018 / Revised: 19 January 2018 / Accepted: 1 February 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Finance, Financial Risk Management and their Applications)
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Abstract

Assuming that a time series incorporates “signal” and “noise” components, we propose a method to estimate the extent of the “noise” component by considering the smoothing properties of the state-space of the time series. A mild degree of smoothing in the state-space, applied using a Kalman filter, allows for noise estimation arising from the measurement process. It is particularly suited in the context of a reputation index, because small amounts of noise can easily mask more significant effects. Adjusting the state-space noise measurement parameter leads to a limiting smoothing situation, from which the extent of noise can be estimated. The results indicate that noise constitutes approximately 10% of the raw signal: approximately 40 decibels. A comparison with low pass filter methods (Butterworth in particular) is made, although low pass filters are more suitable for assessing total signal noise. View Full-Text
Keywords: reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average
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Mitic, P. Noise Reduction in a Reputation Index. Int. J. Financial Stud. 2018, 6, 19.

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